Coronavirus outbreak in December 2019 (COVID-19) is an emerging viral disease that poses major menace to Humans and it’s a crucial need to find the possible treatment strategies. Spike protein (S2), a envelop glycoprotein aids viral entry into the host cells that corresponds to immunogenic ACE2 receptor binding and represents a potential antiviral drug target. Several drugs such as antimalarial, antibiotic, anti-inflammatory and HIV-protease inhibitors are currently undergoing treatment as clinical studies to test the efficacy and safety of COVID-19. Some promising results have been observed with the patients and also with high mortality rate. Hence, there is a need to screen the best CoV inhibitors using insilico analysis. The Molecular methodologies applied in the present study are, Molecular docking, virtual screening, drug-like and ADMET prediction helps to target CoV inhibitors. The results were screened based on docking score, H-bonds, and amino acid interactions. The results shows HIV-protease inhibitors such as cobicistat (-8.3kcal/mol), Darunavir (-7.4kcal/mol), Lopinavir (-9.1kcal/mol) and Ritonavir (-8.0 kcal/mol), anti-inflammatory drugs such as Baricitinib (-5.8kcal/mol), Ruxolitinib (-6.5kcal/mol), Thalidomide (-6.5kcal/mol), antibiotic drugs such as Erythromycin(-9.0kcal/mol) and Spiramycin (-8.5kcal/mol) molecules have good affinity towards spike protein compared to antimalarial drugs Chloroquine (-6.2kcal/mol), Hydroxychloroquine (-5.2kcal/mol) and Artemisinin (-6.8kcal/mol) have poor affinity to spike protein. The insilico pharmacological evaluation shows that these molecules exhibit good affinity of drug-like and ADMET properties. Hence, we propose that HIVprotease, anti-inflammatory and antibiotic inhibitors are the potential lead drug molecules for spike protein and preclinical studies needed to confirm the promising therapeutic ability against COVID-19.

2019年12月的冠状病毒暴发（COVID-19）是一种正在出现的病毒性疾病，对人类构成重大威胁，因此迫切需要找到可能的治疗策略。穗蛋白（S2）是一种包膜糖蛋白，有助于病毒进入宿主细胞，与免疫原性ACE2受体结合相对应，代表潜在的抗病毒药物靶标。作为临床研究，目前正在对几种药物（例如抗疟药，抗生素，抗炎药和HIV蛋白酶抑制剂）进行治疗，以测试COVID-19的功效和安全性。在患者身上观察到一些有希望的结果，并且死亡率很高。因此，需要使用计算机分析来筛选最佳的CoV抑制剂。本研究中应用的分子方法学包括：分子对接，虚拟筛选，类药物和ADMET预测有助于靶向CoV抑制剂。基于对接得分，氢键和氨基酸相互作用筛选结果。结果显示HIV蛋白酶抑制剂，如cobicistat（-8.3kcal / mol），Darunavir（-7.4kcal / mol），Lopinavir（-9.1kcal / mol）和Ritonavir（-8.0 kcal / mol），抗炎药如Baricitinib（-5.8kcal / mol），Ruxolitinib（-6.5kcal / mol），Thalidomide（-6.5kcal / mol），抗生素药物如Erythromycin（-9.0kcal / mol）和Spiramycin（-8.5kcal / mol）分子与抗疟疾药物氯喹（-6.2kcal / mol），羟氯喹（-5.2kcal / mol）和青蒿素（-6.8kcal / mol）相比，对刺突蛋白具有良好的亲和力。电子药理学评估表明，这些分子表现出良好的药物样和ADMET特性亲和力。因此，